From Space to Subsurface: Using Azure AI to Predict Gold Rich Zones

In traditional mineral exploration, identifying gold bearing zones can take months of fieldwork and high cost drilling often with limited success. In our latest project, we flipped the process on its head by using Azure AI and Satellite data to guide geologists before they break ground.

Using Azure AI and Azure Machine Learning, we built an intelligent, automated pipeline that identified high potential zones from geospatial data saving time, cost, and uncertainty.

Here’s a behind the scenes look at how we did it.👇

📡 Step 1: Translating Satellite Imagery into Features

We began with Sentinel-2 imagery covering our Area of Interest (AOI) and derived alteration indices commonly used in mineral exploration, including:

🟤 Clay Index – proxies for hydrothermal alteration
🟥 Fe (Iron Oxide) Index
🌫️ Silica Ratio
💧 NDMI (Normalized Difference Moisture Index)

Using Azure Notebooks and Python, we processed and cleaned the imagery, transforming raw reflectance bands into meaningful geochemical features.

🔍 Step 2: Discovering Patterns with Unsupervised Learning (KMeans)

With feature rich geospatial data prepared, we used unsupervised clustering (KMeans) in Azure Machine Learning Studio to identify natural groupings across the region.

This gave us a first look at the terrain’s underlying geochemical structure one cluster in particular stood out as a strong candidate for gold rich zones.

No geology degree needed: AI finds patterns humans can't see :)

🧠 Step 3: Scaling with Azure AutoML

We then trained a classification model using Azure AutoML to predict these clusters over a dense prediction grid:

✅ 7,200+ data points generated

✅ ~50m resolution grid

✅ 14 km² area of interest

This was executed as a short, early stopping run to minimize cost and optimize training time. Models were trained, validated, and registered using:

Azure Machine Learning
Compute Instance + Compute Cluster
Azure Storage for dataset access

🔬 Step 4: Validation with Field Samples

To ground our predictions, we validated against lab assayed (gold concentration) from field sampling points.

The results?

🔥 The geospatial cluster labeled 'Class 0' by the model showed strong correlation with lab validated gold concentrations, supporting the model's predictive validity.

This gave geologists AI augmented evidence to prioritize areas for further sampling and drilling.

⚖️ Traditional vs AI-based Workflow

🚀 Why Azure?

✅ Azure Machine Learning Studio for AutoML and experiment tracking
✅ Azure Storage for seamless access to geospatial data
✅ Azure OpenAI Service for advanced language understanding, report generation, and enhanced human AI interaction
✅ Azure Notebooks for scripting, preprocessing, and validation
✅ Azure Compute Cluster for scalable, cost effective model training
✅ Model Registry for versioning and deployment

🌍 Key Takeaways

AI turns mineral exploration from reactive guesswork into proactive intelligence.

In our workflow, AI plays a critical role by:

✅ Extracting key geochemical features from satellite imagery
🧠 Identifying patterns using unsupervised learning
🎯 Predicting high potential zones through automated classification
🌍 Delivering full spatial coverage at scale

With Azure AIand Azure ML tools, we’ve built a complete pipeline that supports:

End to end automation; from data prep to model deployment
Faster, more accurate exploration with lower costs
A reusable, scalable solution for global teams

This isn’t just a proof of concept, it’s a production ready framework that empowers geologists with AI driven insights before the first drill hits the ground.

🔗 If you're working in Mining industry, geoscience, AI for Earth, or exploration tech, let’s connect!

We’re on a mission to bring AI deeper into every industry through strategic partnerships and collaborative innovation.